You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.  
  
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.  
  
Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:   
  
Given tanhshrink Architecture (Base PyTorch Implementation)
python
运行
import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, in_features):
        super().__init__()
        torch.manual_seed(42)
        self.linear = nn.Linear(in_features, in_features)

    def forward(self, x):
        x = self.linear(x)
        return x - torch.tanh(x) 

def get_inputs():
    batch_size = 2048
    in_features = 1024
    x = torch.randn(batch_size, in_features)
    return [x]

def get_init_inputs():
    return [1024] 
New Architecture with Custom CUDA Kernels (tanhshrink Optimization)
python
运行
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


UNROLL_FACTOR = 4

tanhshrink_source_optimized = f"""
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <cmath>

__device__ __forceinline__ float tanhshrink_impl(float x) {{
    return x - tanhf(x);
}}


__global__ void tanhshrink_kernel_unroll{UNROLL_FACTOR}(
    const float* __restrict__ input,
    float* __restrict__ output,
    int size
) {{
   
    int idx = blockIdx.x * blockDim.x * {UNROLL_FACTOR} + threadIdx.x;

    #pragma unroll
    for (int i = 0; i < {UNROLL_FACTOR}; ++i) {{
        int current_idx = idx + i * blockDim.x;
        if (current_idx < size) {{
            output[current_idx] = tanhshrink_impl(input[current_idx]);
        }}
    }}
}}

torch::Tensor tanhshrink_cuda_optimized(torch::Tensor input) {{
    TORCH_CHECK(input.is_cuda(), "input must be CUDA tensor");
    TORCH_CHECK(input.dtype() == torch::kFloat32, "input must be float32");

    input = input.contiguous();
    int total_size = input.numel();
    auto output = torch::empty_like(input);

    const int threads_per_block = 512; 
    
    const int blocks = (total_size + threads_per_block * {UNROLL_FACTOR} - 1) / (threads_per_block * {UNROLL_FACTOR});

    tanhshrink_kernel_unroll{UNROLL_FACTOR}<<<blocks, threads_per_block>>>(
        input.data_ptr<float>(),
        output.data_ptr<float>(),
        total_size
    );

    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {{
        throw std::runtime_error("CUDA error: " + std::string(cudaGetErrorString(err)));
    }}
    return output;
}}
"""

tanhshrink_cpp_source_optimized = """
torch::Tensor tanhshrink_cuda_optimized(torch::Tensor input);
"""

tanhshrink_module_optimized = load_inline(
    name="tanhshrink_optimized",
    cpp_sources=tanhshrink_cpp_source_optimized,
    cuda_sources=tanhshrink_source_optimized,
    functions=["tanhshrink_cuda_optimized"],
    extra_cuda_cflags=["-O3", "--use_fast_math"],
    verbose=False
)

class ModelNew(nn.Module):
    def __init__(self, in_features):
        super().__init__()
        torch.manual_seed(42)
        self.linear = nn.Linear(in_features, in_features)
        self.tanhshrink = tanhshrink_module_optimized.tanhshrink_cuda_optimized

    def forward(self, x):
        x = self.linear(x)
        return self.tanhshrink(x)